# Calculate the mean number of directed cycles for each order

# in the randomized network

# -*- coding: utf-8 -*-

from networkx import Graph, DiGraph, simple_cycles, find_cycle, cycle_basis

import networkx as nx

import random

from collections import defaultdict

from lib import *

import csv

# Parameters

# ========================================

n_randomizations = 1000

kmax = 10 # Maximum cycle order

# Load data

# ========================================

# Get the information from both files

dictionary1 = csv2dict("sevaseviene2network.csv", delimiter=";")

# These function transform the dictionary to a directed graph networkx-style

DG1 = DiGraph(dictionary1)

# Remove nodes with out degree 0

remove = [node for node in DG1.nodes if DG1.out_degree(node) <= 0]

DG1.remove_nodes_from(remove)

# Array to store the number of directed cycles found for each order

# accumulated over randomizations

nksum_directed = np.zeros(kmax + 1, dtype=int)

for j_rand in range(n_randomizations):

# Randomize the network

edges = list(DG1.edges())

for edge in edges:

revert_edge = random.randint(0,1)

if revert_edge:

DG1.remove_edge(edge[0], edge[1])

DG1.add_edge(edge[1], edge[0])

# Find the cycles

directed_cycles = list(nx.simple_cycles(DG1))

# Store their distribution

for cycle in directed_cycles:

k = len(cycle)

if k <= kmax:

nksum_directed[k] += 1

# Calculate the mean number of cycles per order

nk_directed = nksum_directed.astype(float)/n_randomizations

# Save the results to file

data = np.vstack(

(np.arange(kmax + 1, dtype=int), nk_directed))

np.savetxt("cycledistribution_rand_sevaseviene.dat", data, fmt="%6d")